Show simple item record

Essays on Optimal Control of Dynamic Systems with Learning

dc.contributor.advisor Sun, Peng
dc.contributor.advisor de Vericourt, Francis
dc.contributor.author Alizamir, Saed
dc.date.accessioned 2013-11-14T19:15:01Z
dc.date.available 2013-11-14T19:15:01Z
dc.date.issued 2013
dc.identifier.uri https://hdl.handle.net/10161/8066
dc.description.abstract <p>This dissertation studies the optimal control of two different dynamic systems with learning: (i) diagnostic service systems, and (ii) green incentive policy design. In both cases, analytical models have been developed to improve our understanding of the system, and managerial insights are gained on its optimal management.</p><p>We first consider a diagnostic service system in a queueing framework, where the service is in the form of sequential hypothesis testing. The agent should dynamically weigh the benefit of performing an additional test on the current task to improve the accuracy of her judgment against the incurred delay cost for the accumulated workload. We analyze the accuracy/congestion tradeoff in this setting and fully characterize the structure of the optimal policy. Further, we allow for admission control (dismissing tasks from the queue without processing) in the system, and derive its implications on the structure of the optimal policy and system's performance.</p><p>We then study Feed-in-Tariff (FIT) policies, which are incentive mechanisms by governments to promote renewable energy technologies. We focus on two key network externalities that govern the evolution of a new technology in the market over time: (i) technological learning, and (ii) social learning. By developing an intertemporal model that captures these dynamics, we investigate how lawmakers should leverage on such effects to make FIT policies more efficient. We contrast our findings against the current practice of FIT-implementing jurisdictions, and also determine how the FIT regimes should depend on specific technology and market characteristics.</p>
dc.subject Operations research
dc.subject Business
dc.subject Public policy
dc.subject Dynamic Programming
dc.subject Feed in Tariff
dc.subject Government Incentive Policies
dc.subject Sequential Hypothesis Testing
dc.subject Service Operations
dc.subject Technology Diffusion
dc.title Essays on Optimal Control of Dynamic Systems with Learning
dc.type Dissertation
dc.department Business Administration


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record